Biologically Informed Algorithms for Modeling Cattle Response to Auditory Cues and Driving Simulations in Virtual Fencing Systems

Hannah James¹Martin Perez²Hunter Adams³Julio Giordano²Mason Peck¹David Erickson^1*

• ¹Cornell University Sibley School of Mechanical and Aerospace Engineering, Ithaca, United States
• ²Cornell University College of Agriculture and Life Sciences, Ithaca, United States
• ³Cornell University School of Electrical and Computer Engineering, Ithaca, United States

Virtual fencing technologies offer a promising alternative to traditional physical barriers for managing livestock, enabling dynamic and non-invasive control over grazing behavior. However, current systems often rely on simplified assumptions about animal responses, neglecting the cognitive, perceptual, and social dynamics that shape cattle behavior in complex herd environments. This study introduces a biologically grounded modeling framework designed to capture core processes influencing cattle response to auditory cues—specifically frequency sensitivity, amplitude saturation, associative learning, habituation, individual behavioral variability, and social influence. These factors are formalized through modular equations; and integrated into agent-based and differential equation simulations to examine individual and herd-level outcomes. By calibrating the model against empirical behavioral data, we reproduce known learning patterns and explore cue effectiveness under variable conditions. The framework offers an interpretable and flexible foundation for optimizing next-generation virtual fencing systems, improving both functional control and animal welfare.